from __future__ import annotations

from collections.abc import MutableSequence



class 							a				:







 """simple docstring"""






 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   None:







        if len(lowercase_				) != degree + 1:
               raise ValueError(
                   """The number of coefficients should be equal to the degree + 1."""				)

        _A	  =    list(lowercase_				)
        _A	  =    degree



 def __add__(   self						,							lowerCAmelCase_				)    ->   Polynomial:







        if self.degree > polynomial_a.degree:
               _A	  =    self.coefficients[:]
               for i in range(polynomial_a.degree + 1				):
                      coefficients[i] += polynomial_a.coefficients[i]
               return Polynomial(self.degree						,							lowercase_				)
        else:
               _A	  =    polynomial_a.coefficients[:]
               for i in range(self.degree + 1				):
                      coefficients[i] += self.coefficients[i]
               return Polynomial(polynomial_a.degree						,							lowercase_				)



 def __sub__(   self						,							lowerCAmelCase_				)    ->   Polynomial:
        return self + polynomial_a * Polynomial(0						,							[-1]				)



 def __neg__(   self				)    ->   Polynomial:
        return Polynomial(self.degree						,							[-c for c in self.coefficients]				)



 def __mul__(   self						,							lowerCAmelCase_				)    ->   Polynomial:
        _A	  =    [0] * (self.degree + polynomial_a.degree + 1)
        for i in range(self.degree + 1				):
               for j in range(polynomial_a.degree + 1				):
                      coefficients[i + j] += (
                          self.coefficients[i] * polynomial_a.coefficients[j]
                      )

        return Polynomial(self.degree + polynomial_a.degree						,							lowercase_				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   int | float:
        _A	  =    0
        for i in range(self.degree + 1				):
               result += self.coefficients[i] * (substitution**i)
        return result



 def __str__(   self				)    ->   str:
        _A	  =    """"""
        for i in range(self.degree						,							-1						,							-1				):
               if self.coefficients[i] == 0:
                      continue
               elif self.coefficients[i] > 0:
                      if polynomial:
                             polynomial += " + "
               else:
                      polynomial += " - "

               if i == 0:
                      polynomial += str(abs(self.coefficients[i]				)				)
               elif i == 1:
                      polynomial += str(abs(self.coefficients[i]				)				) + "x"
               else:
                      polynomial += str(abs(self.coefficients[i]				)				) + "x^" + str(lowercase_				)

        return polynomial



 def __repr__(   self				)    ->   str:
        return self.__str__()



 def 	UpperCAmelCase				(   self				)    ->   Polynomial:
        _A	  =    [0] * self.degree
        for i in range(self.degree				):
               _A	  =    self.coefficients[i + 1] * (i + 1)
        return Polynomial(self.degree - 1						,							lowercase_				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_ = 0				)    ->   Polynomial:
        _A	  =    [0] * (self.degree + 2)
        _A	  =    constant
        for i in range(self.degree + 1				):
               _A	  =    self.coefficients[i] / (i + 1)
        return Polynomial(self.degree + 1						,							lowercase_				)



 def __eq__(   self						,							lowerCAmelCase_				)    ->   bool:







        if not isinstance(lowercase_						,							lowercase_				):
               return False

        if self.degree != polynomial_a.degree:
               return False

        for i in range(self.degree + 1				):
               if self.coefficients[i] != polynomial_a.coefficients[i]:
                      return False

        return True



 def __ne__(   self						,							lowerCAmelCase_				)    ->   bool:
        return not self.__eq__(lowercase_				)





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




